1. Field of the Invention
This invention relates to high performance computing systems, and more particularly, to efficiently performing program instrumentation.
2. Background
An understanding of the dynamic behavior of software applications allows software programmers to write the applications in both an efficient and a high-performance manner. For this reason, software programmers at times add additional code to developing applications. The additional code may include instrumentation code and analysis code that communicates statistics and other information about the behavior of the application as it is processed. Patterns and particular events may be identified and characterized. However, as both the speed and the functionality of processors increase, it has become more difficult to collect information about the dynamic behavior of the applications.
The collection of information about application dynamic behavior may include a large number of trace instructions, a large amount of statistics, and an appreciable amount of overhead to perform the collection. The additional code may reduce the execution time of an application by an order of magnitude. A computer or server may run particular code for hours to test all major code paths. Straightforward instrumentation may increase the run time to days or even weeks.
Due to the problems identified above, the instrumentation and analysis code may be sampled to reduce the drawbacks of collecting dynamic behavior information. A relatively small percentage of the dynamically encountered instrumentation code is actually executed. The selection of when to execute the dynamically encountered instrumentation code may be performed in a random manner. Unfortunately, the selection process or performing the sampling decisions consumes an appreciable amount of time and cost.
Generating and comparing random numbers in software is non-trivial. Similarly, moving the sampling decisions to hardware consumes on-die real estate as circuitry is added to perform random number generation and connecting the results to other parts of the processor. Additionally, new instructions may be added to the instruction set architecture (ISA) to offer support, which is a non-trivial effort. Another approach may include using hardware to randomly tag an instruction and gather microarchitecture-level information about the processing of the tagged instruction. However, such an approach utilizes hard-coded analysis in the processor, rather than user-defined custom instrumentation code. Further, such an approach analyzes a single instruction versus multiple instructions of a software-based approach.
In view of the above, efficient methods and systems for efficiently performing program instrumentation are desired.